{"list":"other","total":191,"rows":[{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"id":"2205.11487","title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","created":"2022-05-23","cited_by_count":6355},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"2108.07258","title":"On the Opportunities and Risks of Foundation Models","created":"2021-08-16","cited_by_count":4033},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"id":"2303.18223","title":"A Survey of Large Language Models","created":"2023-03-31","cited_by_count":2576},{"id":"1412.3555","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","created":"2014-12-11","cited_by_count":2367},{"id":"1709.07871","title":"FiLM: Visual Reasoning with a General Conditioning Layer","created":"2017-09-22","cited_by_count":2333},{"id":"1606.04080","title":"Matching Networks for One Shot Learning","created":"2016-06-13","cited_by_count":2255},{"id":"2107.13586","title":"Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing","created":"2021-07-28","cited_by_count":2234},{"id":"1607.00653","title":"node2vec: Scalable Feature Learning for Networks","created":"2016-07-03","cited_by_count":1918},{"id":"2006.05525","title":"Knowledge Distillation: A Survey","created":"2020-06-09","cited_by_count":1603},{"id":"1403.6652","title":"DeepWalk: Online Learning of Social Representations","created":"2014-03-26","cited_by_count":1547},{"id":"1901.05555","title":"Class-Balanced Loss Based on Effective Number of Samples","created":"2019-01-16","cited_by_count":1245},{"id":"2004.06165","title":"Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks","created":"2020-04-13","cited_by_count":1214},{"id":"1803.02999","title":"On First-Order Meta-Learning Algorithms","created":"2018-03-08","cited_by_count":1018},{"id":"1905.12265","title":"Strategies for Pre-training Graph Neural Networks","created":"2019-05-29","cited_by_count":971},{"id":"2004.05439","title":"Meta-Learning in Neural Networks: A Survey","created":"2020-04-11","cited_by_count":950},{"id":"1904.05068","title":"Relational Knowledge Distillation","created":"2019-04-10","cited_by_count":889},{"id":"1910.09217","title":"Decoupling Representation and Classifier for Long-Tailed Recognition","created":"2019-10-21","cited_by_count":809},{"id":"1606.07947","title":"Sequence-Level Knowledge Distillation","created":"2016-06-25","cited_by_count":801},{"id":"1806.08804","title":"Hierarchical Graph Representation Learning with Differentiable Pooling","created":"2018-06-22","cited_by_count":787},{"id":"1509.09292","title":"Convolutional Networks on Graphs for Learning Molecular Fingerprints","created":"2015-09-30","cited_by_count":754},{"id":"1903.07293","title":"Heterogeneous Graph Attention Network","created":"2019-03-18","cited_by_count":734},{"id":"2002.00388","title":"A Survey on Knowledge Graphs: Representation, Acquisition and Applications","created":"2020-02-02","cited_by_count":643},{"id":"1503.03578","title":"LINE: Large-scale Information Network Embedding","created":"2015-03-12","cited_by_count":582},{"id":"1710.05381","title":"A systematic study of the class imbalance problem in convolutional neural networks","created":"2017-10-15","cited_by_count":513},{"id":"1905.05178","title":"Graph U-Nets","created":"2019-05-11","cited_by_count":492},{"id":"2110.04596","title":"Deep Long-Tailed Learning: A Survey","created":"2021-10-09","cited_by_count":417},{"id":"1901.03407","title":"Deep Learning for Anomaly Detection: A Survey","created":"2019-01-10","cited_by_count":358},{"id":"2106.06947","title":"Graph Neural Network-Based Anomaly Detection in Multivariate Time Series","created":"2021-06-13","cited_by_count":301},{"id":"1709.07604","title":"A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications","created":"2017-09-22","cited_by_count":260},{"id":"1810.10147","title":"FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation","created":"2018-10-24","cited_by_count":254},{"id":"2008.08692","title":"Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters","created":"2020-08-19","cited_by_count":250},{"id":"2106.07178","title":"A Comprehensive Survey on Graph Anomaly Detection with Deep Learning","created":"2021-06-14","cited_by_count":230},{"id":"2205.15508","title":"Rethinking Graph Neural Networks for Anomaly Detection","created":"2022-05-31","cited_by_count":187},{"id":"1404.4679","title":"Graph-based Anomaly Detection and Description: A Survey","created":"2014-04-18","cited_by_count":183},{"id":"1911.02855","title":"Dice Loss for Data-imbalanced NLP Tasks","created":"2019-11-07","cited_by_count":182},{"id":"2103.00113","title":"Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning","created":"2021-02-27","cited_by_count":177},{"id":"2112.14936","title":"Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks","created":"2021-12-30","cited_by_count":176},{"id":"2009.01454","title":"Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information","created":"2020-09-03","cited_by_count":168},{"id":"2009.02040","title":"Multivariate Time-series Anomaly Detection via Graph Attention Network","created":"2020-09-04","cited_by_count":167},{"id":"2302.08043","title":"GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks","created":"2023-02-16","cited_by_count":167},{"id":"2205.06743","title":"A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities","created":"2022-05-13","cited_by_count":163},{"id":"2103.08826","title":"GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks","created":"2021-03-16","cited_by_count":147},{"id":"1905.10674","title":"Compositional Fairness Constraints for Graph Embeddings","created":"2019-05-25","cited_by_count":126},{"id":"1908.00413","title":"MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation","created":"2019-07-31","cited_by_count":121},{"id":"1905.09718","title":"Meta-GNN: On Few-shot Node Classification in Graph Meta-learning","created":"2019-05-23","cited_by_count":105},{"id":"1910.07124","title":"FewRel 2.0: Towards More Challenging Few-Shot Relation Classification","created":"2019-10-16","cited_by_count":94},{"id":"2108.05233","title":"EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks","created":"2021-08-11","cited_by_count":92},{"id":"2006.12739","title":"Graph Prototypical Networks for Few-shot Learning on Attributed Networks","created":"2020-06-23","cited_by_count":90},{"id":"2006.07889","title":"Graph Meta Learning via Local Subgraphs","created":"2020-06-14","cited_by_count":89},{"id":"2005.00625","title":"Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection","created":"2020-05-01","cited_by_count":82},{"id":"2102.07916","title":"Few-Shot Graph Learning for Molecular Property Prediction","created":"2021-02-16","cited_by_count":81},{"id":"2206.10071","title":"BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs","created":"2022-06-21","cited_by_count":80},{"id":"2107.05315","title":"Contrastive Learning for Cold-Start Recommendation","created":"2021-07-12","cited_by_count":79},{"id":"1808.09040","title":"One-Shot Relational Learning for Knowledge Graphs","created":"2018-08-27","cited_by_count":78},{"id":"2204.09888","title":"Fairness in Graph Mining: A Survey","created":"2022-04-21","cited_by_count":76},{"id":"2006.02046","title":"Fairness-Aware Explainable Recommendation over Knowledge Graphs","created":"2020-06-03","cited_by_count":74},{"id":"1909.01515","title":"Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs","created":"2019-09-04","cited_by_count":73},{"id":"2106.15535","title":"Subgroup Generalization and Fairness of Graph Neural Networks","created":"2021-06-29","cited_by_count":69},{"id":"2104.14210","title":"FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning","created":"2021-04-29","cited_by_count":68},{"id":"2112.10063","title":"Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation","created":"2021-12-19","cited_by_count":67},{"id":"2102.11165","title":"Few-shot Network Anomaly Detection via Cross-network Meta-learning","created":"2021-02-22","cited_by_count":64},{"id":"1910.03053","title":"Graph Few-shot Learning via Knowledge Transfer","created":"2019-10-07","cited_by_count":63},{"id":"1911.11298","title":"Few-Shot Knowledge Graph Completion","created":"2019-11-26","cited_by_count":63},{"id":"2206.03426","title":"Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage","created":"2022-06-07","cited_by_count":61},{"id":"1906.02319","title":"DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification","created":"2019-06-05","cited_by_count":59},{"id":"2111.04840","title":"Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods","created":"2021-11-08","cited_by_count":58},{"id":"2201.03662","title":"Learning Fair Node Representations with Graph Counterfactual Fairness","created":"2022-01-10","cited_by_count":58},{"id":"2005.07427","title":"Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs","created":"2020-05-15","cited_by_count":56},{"id":"2110.04099","title":"Topology-Imbalance Learning for Semi-Supervised Node Classification","created":"2021-10-08","cited_by_count":56},{"id":"2002.11442","title":"DeBayes: a Bayesian Method for Debiasing Network Embeddings","created":"2020-02-26","cited_by_count":55},{"id":"2005.10150","title":"GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection","created":"2020-05-20","cited_by_count":54},{"id":"2106.02817","title":"ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks","created":"2021-06-05","cited_by_count":51},{"id":"2207.04869","title":"Graph-based Molecular Representation Learning","created":"2022-07-08","cited_by_count":51},{"id":"1812.04361","title":"RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information","created":"2018-12-11","cited_by_count":47},{"id":"2006.06648","title":"Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction","created":"2020-06-11","cited_by_count":47},{"id":"2306.06063","title":"Virtual Node Tuning for Few-shot Node Classification","created":"2023-06-09","cited_by_count":46},{"id":"2205.13845","title":"Raising the Bar in Graph-level Anomaly Detection","created":"2022-05-27","cited_by_count":45},{"id":"2205.13947","title":"Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer","created":"2022-05-27","cited_by_count":43},{"id":"2206.11972","title":"Task-Adaptive Few-shot Node Classification","created":"2022-06-23","cited_by_count":43},{"id":"2010.09638","title":"Adaptive Attentional Network for Few-Shot Knowledge Graph Completion","created":"2020-10-19","cited_by_count":42},{"id":"2012.07064","title":"Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation","created":"2020-12-13","cited_by_count":42},{"id":"2112.05355","title":"LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks","created":"2021-12-10","cited_by_count":42},{"id":"2107.07994","title":"Property-Aware Relation Networks for Few-Shot Molecular Property Prediction","created":"2021-07-16","cited_by_count":41},{"id":"2112.12819","title":"Graph Few-shot Class-incremental Learning","created":"2021-12-23","cited_by_count":41},{"id":"2206.12917","title":"TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification","created":"2022-06-26","cited_by_count":41},{"id":"1602.04844","title":"Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs","created":"2016-02-15","cited_by_count":37},{"id":"1908.03849","title":"SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks","created":"2019-08-11","cited_by_count":37},{"id":"2105.02725","title":"CrossWalk: Fairness-enhanced Node Representation Learning","created":"2021-05-06","cited_by_count":34},{"id":"2104.13095","title":"Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion","created":"2021-04-27","cited_by_count":32},{"id":"2110.14170","title":"Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding","created":"2021-10-27","cited_by_count":32}],"next_cursor":"100"}